#!/bin/bash

################基础配置参数，需要模型审视修改##################
# 必选字段(必须在此处定义的参数): Network batch_size RANK_SIZE
# 网络名称，同目录名称
Network="DLRM"

# 数据集路径,保持为空,不需要修改
data_path=""

# 训练epoch
train_epochs=1
# 指定训练所使用的npu device卡id
device_id=0
batch_size=32768

# 参数校验，data_path为必传参数，其他参数的增删由模型自身决定；此处新增参数需在上面有定义并赋值
for para in $*
do
    if [[ $para == --device_id* ]];then
        device_id=`echo ${para#*=}`
    elif [[ $para == --data_path* ]];then
        data_path=`echo ${para#*=}`
    fi
done

# 校验是否传入data_path,不需要修改
if [[ $data_path == "" ]];then
    echo "[Error] para \"data_path\" must be confing"
    exit 1
fi
# 校验是否指定了device_id,分动态分配device_id与手动指定device_id,此处不需要修改
if [ $ASCEND_DEVICE_ID ];then
    echo "device id is ${ASCEND_DEVICE_ID}"
elif [ ${device_id} ];then
    export ASCEND_DEVICE_ID=${device_id}
    echo "device id is ${ASCEND_DEVICE_ID}"
else
    "[Error] device id must be config"
    exit 1
fi



###############指定训练脚本执行路径###############
# cd到与test文件夹同层级目录下执行脚本，提高兼容性；test_path_dir为包含test文件夹的路径
cur_path=`pwd`
cur_path_last_dirname=${cur_path##*/}
if [ x"${cur_path_last_dirname}" == x"test" ];then
    test_path_dir=${cur_path}
    cd ..
    cur_path=`pwd`
else
    test_path_dir=${cur_path}/test
fi


#################创建日志输出目录，不需要修改#################
if [ -d ${test_path_dir}/output/${ASCEND_DEVICE_ID} ];then
    rm -rf ${test_path_dir}/output/${ASCEND_DEVICE_ID}
    mkdir -p ${test_path_dir}/output/$ASCEND_DEVICE_ID
else
    mkdir -p ${test_path_dir}/output/$ASCEND_DEVICE_ID
fi
#################启动训练脚本#################
#训练开始时间，不需要修改
start_time=$(date +%s)
# 非平台场景时source 环境变量
check_etp_flag=`env | grep etp_running_flag`
etp_flag=`echo ${check_etp_flag#*=}`
if [ x"${etp_flag}" != x"true" ];then
    source ${test_path_dir}/env_npu.sh
fi
export WORLD_SIZE=8
export MASTER_ADDR='127.0.0.1'
export MASTER_PORT='12581'

data_file=${data_path}

RANK_ID_START=0

for((RANK_ID=$RANK_ID_START;RANK_ID<$((WORLD_SIZE+RANK_ID_START));RANK_ID++))
do
    echo ${RANK_ID}
    export RANK=${RANK_ID}
    KERNEL_NUM=$(($(nproc)/8))
    PID_START=$((KERNEL_NUM * RANK_ID))
    PID_END=$((PID_START + KERNEL_NUM - 1))
    taskset -c $PID_START-$PID_END python3 dlrm_s_pytorch.py --arch-sparse-feature-size=64 \
                                                                --arch-mlp-bot="13-512-256-64" \
                                                                --arch-mlp-top="512-512-256-1" \
                                                                --max-ind-range=10000000 \
                                                                --data-generation=dataset \
                                                                --data-set=terabyte \
                                                                --raw-data-file=${data_file} \
                                                                --processed-data-file=./input/terabyte_processed.npz \
                                                                --loss-function=bce \
                                                                --round-targets=True \
                                                                --learning-rate=1 \
                                                                --mini-batch-size=${batch_size} \
                                                                --print-freq=1 \
                                                                --print-time \
                                                                --test-mini-batch-size=16384 \
                                                                --test-num-workers=16 \
                                                                --use-gpu \
                                                                --local_rank=$RANK_ID \
                                                                --dist-backend='nccl' \
                                                                --test-freq=10240 \
                                                                --data-sub-sample-rate=0.875 \
                                                                --mlperf-logging \
                                                                --performance \
                                                                --memory-map > ${test_path_dir}/output/${ASCEND_DEVICE_ID}/train_$RANK_ID.log 2>&1 &
done
wait


##################获取训练数据################
#训练结束时间，不需要修改
end_time=$(date +%s)
e2e_time=$(( $end_time - $start_time ))

#结果打印，不需要修改
echo "------------------ Final result ------------------"
#输出性能FPS，需要模型审视修改
FPS=`grep -a 'Finished'  ${test_path_dir}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk -F "," '{print $2}'|tail -${FPS_LIST} |awk '{a+=$1} END { if (NR !=0) printf("%.3f",a/NR)}'`
#打印，不需要修改
FPS=$(awk 'BEGIN{printf "%.2f\n", '${batch_size}*1000'/'${FPS}'}')
echo "Final Performance sec/images : $FPS"

#输出训练精度,需要模型审视修改
train_accuracy=`grep -a 'recall'  ${test_path_dir}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk -F " " '{print $10}'|awk -F "," '{print $1}'|tail -1`
#打印，不需要修改
echo "Final Train Accuracy : ${train_accuracy}"
echo "E2E Training Duration sec : $e2e_time"

#性能看护结果汇总
#训练用例信息，不需要修改
BatchSize=${batch_size}
DeviceType=`uname -m`
CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'acc'

##获取性能数据，不需要修改
#吞吐量
ActualFPS=${FPS}

#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视
grep 'Finished' ${test_path_dir}/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|awk -F "," '{print $3}'|awk -F " " '{print $2}' >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt

#最后一个迭代loss值，不需要修改
ActualLoss=`awk 'END {print}'  ${test_path_dir}/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt`

#关键信息打印到${CaseName}.log中，不需要修改
echo "Network = ${Network}" >  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "RankSize = ${RANK_SIZE}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "BatchSize = ${BatchSize}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "DeviceType = ${DeviceType}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "CaseName = ${CaseName}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "ActualFPS = ${ActualFPS}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "TrainAccuracy = ${train_accuracy}" >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "ActualLoss = ${ActualLoss}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log